Contact Info.
Office Hours: Tuesdays 3 pm - 5 pm on Zoom and/or in the office
Interpret and evaluate statistical theories and methods of geospatial data analytics
Use R or other programming language to design and implement typical geospatial analysis methods
Effectively visualize and interpret the outcome of geospatial analysis
Concepts in statistics and spatial analysis
Concepts in statistics and spatial analysis
Workbooks or practicals
Lab work for this course will be done using RStudio IDE
Available online at the following website: https://posit.co/download/rstudio-desktop/
R studio is a free and well designed interface for using R.
GEOG4070:
GEOG5070:
Descriptive statistics: concerned with the summarization, organization and presentation of data;
Types: Numerical, Tabular, Graphical, Cartographic (e.g., choropleth mapping)
The scientific method gives us a means by which to approach the problems we wish to solve
The core of this method is the forming and testing of hypotheses
Statistics helps us choose how to observe, describe, test, and organize!
John Snow (1813-1858)
A British physician, considered “father of modern epidemiology”
Conducted disease mapping for the London cholera epidemic in 1854
#dev.off()
plot(Snow.deaths[,c("x","y")], col="red", pch=19, cex=.7,xlab="", ylab="", xlim=c(3,20), ylim=c(3,20))
slist <- split(Snow.streets[,c("x","y")],as.factor(Snow.streets[,"street"]))
invisible(lapply(slist, lines, col="grey"))
require(KernSmooth)
kde2d <- bkde2D(Snow.deaths[,2:3], bandwidth=c(0.5,0.5))
contour(x=kde2d$x1, y=kde2d$x2,z=kde2d$fhat, add=TRUE)
clrs=colorRampPalette(c(rgb(0,0,1,0), rgb(0,0,1,1)), alpha = TRUE)(20)
image(x=kde2d$x1, y=kde2d$x2,z=kde2d$fhat, add=TRUE,col=clrs)
contour(x=kde2d$x1, y=kde2d$x2,z=kde2d$fhat, add=TRUE)counts_voronoi <- neighborhoodVoronoi()
# Create table of "Actual vs Expected"
voronoi_actvpred <- matrix(0,nrow=14,ncol=5)
colnames(voronoi_actvpred) <- c("pump.id","Actual","Area %","Expected","Pearson")
rownames(voronoi_actvpred) <- c("Market Place","Adam and Eve Court","Berners Street","Newman Street","Marlborough Mews",
"Little Marlborough Street","Broad Street","Warwick Street","Bridle Street","Rupert Street",
"Dean Street","Tichborne Street","Vigo Street","Total")
# Populate the table from the "counts_voronoi"
voronoi_actvpred[1:13,"pump.id"] <- counts_voronoi$expected.data[,1]
voronoi_actvpred[1:13,"Actual"] <- summary(counts_voronoi) # the actual counts (summing across neighborhoods??)
voronoi_actvpred[1:13,"Area %"] <- counts_voronoi$expected.data[,3] # pump neighborhoods as % of total area
voronoi_actvpred["Total","Actual"] <- sum(voronoi_actvpred[1:13,"Actual"]) # Calculate total counts and area (area should be 1.0)
voronoi_actvpred[1:13,"Expected"] <- voronoi_actvpred[1:13,"Area %"] * voronoi_actvpred["Total","Actual"] # Calculate Expected = %area * total counts
voronoi_actvpred[1:13,"Pearson"] <- ((voronoi_actvpred[1:13,"Actual"] - voronoi_actvpred[1:13,"Expected"])^2) /
voronoi_actvpred[1:13,"Expected"]
voronoi_actvpred[1:13,"Area %"] <- 100*voronoi_actvpred[1:13,"Area %"] # Convert from decimal to percent
voronoi_actvpred["Total",c("Expected","Pearson","Area %")] <- colSums(voronoi_actvpred[1:13,c("Expected","Pearson","Area %")]) | pump.id | Actual | Area % | Expected | Pearson | |
|---|---|---|---|---|---|
| Market Place | 1 | 0 | 6.1 | 19.5 | 19.5 |
| Adam and Eve Court | 2 | 1 | 1.9 | 6.2 | 4.4 |
| Berners Street | 3 | 10 | 4.4 | 14.0 | 1.1 |
| Newman Street | 4 | 13 | 9.5 | 30.4 | 10.0 |
| Marlborough Mews | 5 | 3 | 8.2 | 26.5 | 20.8 |
| Little Marlborough Street | 6 | 39 | 12.4 | 39.9 | 0.0 |
| Broad Street | 7 | 182 | 8.5 | 27.2 | 881.5 |
| Warwick Street | 8 | 12 | 6.9 | 22.1 | 4.6 |
| Bridle Street | 9 | 17 | 4.8 | 15.5 | 0.1 |
| Rupert Street | 10 | 38 | 5.9 | 19.0 | 19.1 |
| Dean Street | 11 | 2 | 7.7 | 24.6 | 20.8 |
| Tichborne Street | 12 | 2 | 9.2 | 29.7 | 25.8 |
| Vigo Street | 13 | 2 | 14.5 | 46.4 | 42.5 |
| Total | 0 | 321 | 100.0 | 321.0 | 1050.2 |
Now, we have to ensure that you have installed R studio and R that we will be using all semester.
Next week we will start talking about the basic terms and concepts in statistical analysis and the characteristics of spatial data and data classification.
On next Thursday, we will start working on the first practical on getting started with R studio!